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rm(list = ls())
library(data.table)
library(tidyr)
library(maps)
library(haven)
library(ggplot2)
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library(readxl)
countryfacts <- read_excel("/Users/cristinacandido/Documents/Github/risk_wvs/code/Hardship_complete_2024.xlsx")
countryfactsNA
NAcountryfacts$homiciderate=log(countryfacts$homiciderate)
countryfacts$gdp=log(countryfacts$gdp)
countryfacts$infantmortality=log(countryfacts$infantmortality)
countryfacts$lifeexpectancy=log(countryfacts$lifeexpectancy)
countryfacts
# Reverse Codierung
countryfacts$homiciderate=scale(countryfacts$homiciderate)
countryfacts$gdp=scale(-countryfacts$gdp)
countryfacts$infantmortality=scale(countryfacts$infantmortality)
countryfacts$lifeexpectancy=scale(-countryfacts$lifeexpectancy)
countryfacts$gini=scale(countryfacts$gini)
countryfacts$femalemale_primedu=scale(-countryfacts$femalemale_primedu)
countryfacts
countryfacts$hardship_index=(countryfacts$homiciderate+countryfacts$gdp+countryfacts$gini+countryfacts$lifeexpectancy+countryfacts$infantmortality+countryfacts$femalemale_primedu)/6
countryfacts
# Laura added this code
library(dplyr)
# Impute missing values in the hardship indicators using the median
countryfacts <- countryfacts %>%
mutate(across(c("homiciderate", "gdp", "infantmortality", "lifeexpectancy", "gini", "femalemale_primedu"),
~ifelse(is.na(.), median(., na.rm = TRUE), .)))
countryfacts
# Use the mutate function to change the country name
countryfacts <- countryfacts %>%
mutate(country = ifelse(label == "Serbia and Montenegro", "Serbia", label))
countryfacts# Data of Wave 5
WV5_data <- readRDS("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/F00007944-WV5_Data_R_v20180912.rds")
# Convert WV5_data-object in data.frame
WV5_data_df <- as.data.frame(WV5_data)
# show first five columns
WV5_data_df#rename the variables
data5 <- WV5_data_df %>%
rename(gender = V235, age = V237, country_code = V2, wave = V1, risktaking = V86, children = V56, married = V55, employed = V241, education = V238)
data5